Pertti Koukkari and Risto Pajarre

VTT – Technical Research Centre of FinlandP.O. Box 1000, FIN-02044 VTT, FINLAND

Use of ChemSheet and Kilnsimu forMaterial and Energy Saving Processes

GTT Thermochemistry Workshop, Herzogenrath 12.7. 2012

Outline of presentation

Recent progress with ChemSheet and Kilnsimu

ChemSheet and Kilnsimu using full equilibrium models- SteaMax for mixed and biofuel boilers - Zinc recovery in the Waelz kiln (news from 2011)- ChemSheet modelling of acid mine drainage

Use of additional constraints – CFE method

Selected examples with CFE- surface energies of alloys and mixtures- back diffusion and paraequilibria in solidification- simulation of rotary kilns as non-equilibrium reactors

Summary

Acknowledgements

Development of advanced multi-phase methods and algorithms with Gibbsenergy minimization

Constrained Gibbs free energy techniques for forced equilibria and rate-controlled multi-phase processes

Robust and practice-oriented simulation based on many years of industrialexperience

VTT software:ChemSheet – Gibbs’ian models in ExcelTM spreadsheetsBALAS – Flowsheet simulation of pulp & papermakingKilnSimu – Multi-phase chemistry for rotary drumsRatemix - Reaction rate-controlled Gibbs’ian systemsChemApp – Thermodynamic multi-phase applications

ChemSheet & KilnSimu licenced & jointly developed with GTT

Thermochemical process modeling at VTT

Recent progress with ChemSheet and KilnSimu models

Application Program Type CFE-use*

Problem solved Advantage

SteaMax for bio & mixed fuel boilers

ChemSheet EQ - Material corrosivity in heat exchangers

Competitive edge of several M€ (boiler design)

TiO2 burner ChemSheet Non-EQ

Kinetic Scale-up, location secondary feed

Resource intensity without extra investment

SDHL Waelz kiln(Zn recycling)

ChemSheet(KilnSimu)

EQNonEQ

- Use of autogenic heat

Resource intensity with new operationalmode

Paper machine neutral conversion

ChemSheet PartialEQ

Donnan Ion exchange and pH of fibrous pulp

Control of hemicaldosage for stable operation

ChemPool for nuclear safety

ChemSheet(extended)

EQ - pH buffering of drywell/wetwellsafety systems

Design & scale-up e.g. by U.S. NRC-standards

Alloy nanoparticles

ChemSheet PartialEQ

Surface Phase behaviour and melting

Design of functional nanomaterials

Surface properties of alloys

ChemSheet PartialEQ

Surface Surface tension and melting

Design of functional composite matrixes

Rotary kilns for cement, pigment, pig iron etc.

KilnSimu Non-EQ

Kinetic ChemEner profiles in kilns with variable feed

Scale-up and sustainable operation of rotary kilns

Flame structure in rotary kilns

Fluent-KilnSimu

Non-EQ

(kinetic) 2-way coupling of kiln bed and gas

Reduce kiln carbon footprint

ProMine to recover metal values from AMD

ChemSheet EQ - Interpretation of mine drainage titration

Recovery of value-added nanoparticles from mine waters

Paraequilibria ChemSheet P-EQ Paraeq. Phase formationin solidification

Improved steel / alloy property control

Magnetic eq. ChemSheet To be published

*CFE = ’Constrained Free Energy’ applies additional immaterial constraints in Gibbs energy minimisation

ChemSheet expert system for boilerdesign by ash and flue gas chemistry Steam capacity of bio- and waste fuel

boilers Exact switch position from stainless to

carbon steel in the superheater Maximum material temperatures

for low-alloy and stainless steels Total amount of steel in the heat

exchanger may exceed 500 t / unit, giving a significant competitiveadvance to SteaMax designs

SteaMax expert system for biofuel boilers

Enestam, S., Niemi, J. and Mäkelä, K.: STEAMAX –A novel approach for corrosion prediction, material selectionand optimization of steam parameters for boilers firing fuel and fuel mixtures derived from biomass and waste The Clearwater Coal Conference, Clearwater, Florida, June 2008

Enestam, S., Koukkari, P.: SteaMax expert system for superheater design, in VTT Research Notes 2506, Espoo 2009

Enestam S.: Corrosivity of hot flue gases in the fluidized bed combustion of recovered waste wood, Report 11-04, (2011),Åbo Akademi, Department of Chemical Engineering (thesis).

1,E-08

1,E-07

1,E-06

1,E-05

1,E-04

1,E-03

350

400

450

500

550

600

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

Flue gas temperature [C]

The

rela

tive

corr

osiv

ity o

f the

fuel

/fu

el m

ixtu

re

RDF50% Stem wood + 50% RDFBark (spruce)Stem wood

1000

100

10

1

0,1

0,01

Recycling zinc from steel production

40 % of steel produced in Europe is recycled from scrap in electric arc furnaces (EAF)

in EAF Zn evaporates and is later collected as ZnO dust (35 % Zn)

Zn is then recovered in the Waelz process

EU27 Zn recovery is ca 250 000 t/yr

1) H2O(l)->H2O(g)•ZnO(s)•FeO(s)•PbO(s)•SiO2(s)•MgO(s)

•CaCO3(s)•C(s)

•H2O(l)

•N2(g) •CO2(g)•CO(g)•H2O(g)•ZnO(s)•Pb(l)

•H2O(g)

•N2(g) •O2(g)

•H2O(g) •CO2(g)

Waelz feed

Fuel gas

Products

Exit gas

•Zn(g), CO (g)

•Zn(g)+0.5O2(g)->ZnO(s)

•Fe(s)•CaO(s)•Pb(l)

•ZnO(s)•C(s)

2) CaCO3(s)->CaO(s)+CO2 (g)

3) FeO(s)+C(s)->Fe(s)+CO(g)

4) ZnO(s)+C(s)->Zn(g)+CO (g)

5) C(s)+0.5O2(g)->CO (g)

•CO2(g)

•CO(g)+O2(g)<->CO2(g) •CO(g)+O2(g)<->CO2(g)

•O2(g) •CO (g)

•Slag(l)•CaO•SiO2•MgO•Al2O3•FeO

Schematic view of the Waelz kiln chemistry

A 4-stage ChemSheet model:

Zn-oxide reduction the oxidation of Waelz-slag (including

autogenic oxidation of reduced iron byadditional air-blasting)

oxidation of reduced Zn-vapour to Zn-oxide dust

finally, model for the offgas cooling and cleaning.

Resource efficiency for Zn-recycling

ChemSheet model of the SDHL Waelz kiln*

*Jürgen Korb GTT-Technologies, Freiberg/Sachsen at GTT Workshop, Herzogenrath 2003

The model is used to find adequate operating temperatures for the reduction and oxidation stages in the bed.

Furthermore, the composition of the gas and the yield of both zinc and lead in the vapour phase become calculated.

Contribution to development of BAT

For the development of the so-called SDHL technology that allows about 40 % savings of CO2-emissions, the Waelz-process has been awarded already in 2003 the Environmental Award of Saxony, a federal state in Germany.

"Since then, the Waelz-process has been improved permanently. Today it is regarded as the best available technology for recycling EAF dusts", says Dipl.-Ing. Andeas Ruh, Befesa Steel Services GmbH.

‘Zinc meets steel: a strong team in climate protectionand resource efficiency’, Leoben 10.05.2011;www.foundry-planet.com/pt/detailview-corporate/9710.html.

Multicomponent modelling of acid mine drainage

acid mine water drainage (AMD) is a major challenge at both operatingand closed mining and mineral processing sites

slow oxidation of exposed sulphide minerals leads to formation of (tio)sulphatesand eventually sulphuric acid

dissolution of sulphates to ground water and/or rain leads to acidic leakage toenvironment => ecological problems

Selective precipitation of metals from mine waters

neutralisation of AMD leads to multicomponent precipitation with little practicalvalue

selective precipitation could provide re-usable fractions mine water titration curves will give valuable information for selective precipitations dilute solutions give a fair performance of Pitzer GE-models

Most metal ions form specific stoichiometric compounds that begin to formonce the pH of the solution exceeds a compound-specific threshold

=> possible to selectively recover metals by model-based pH control

Recovery of metal fractions from mine water samples

Values are percentages of total amountof each metal

The aim was to recover Zn and Cu from the waste water at high yield.

Initial concentrations of Zn and Cu were 350 and 14 mg/L

Titration yields 97% Zn and 79% Cu (pH fraction 4.9 to 8)

Exclusion of CO2 will enable recoveryof Mn and Mg as separate fractions

Images from stereomicroscopepH5 pH8 pH11

Räsänen, L., Blomberg, P., Mäki, T. , Koukkari P.: Quantitative Analysis of Multi-Component Acid-Base Titration and Selective Recovery of Metals from Acidic Mine Waters (to be published at ISSP 2012, Xining, China )

Application Program Type CFE-use*

Problem solved Advantage

SteaMax for bio & mixed fuel boilers

ChemSheet EQ - Material corrosivity in heat exchangers

Competitive edge of several M€ (boiler design)

TiO2 burner ChemSheet Non-EQ

Kinetic Scale-up, location secondary feed

Resource intensity without extra investment

SDHL Waelz kiln(Zn recycling)

ChemSheet(KilnSimu)

EQNonEQ

- Use of autogenic heat

Resource intensity with new operationalmode

Paper machine neutral conversion

ChemSheet PartialEQ

Donnan Ion exchange and pH of fibrous pulp

Control of hemicaldosage for stable operation

ChemPool for nuclear safety

ChemSheet(extended)

EQ - pH buffering of drywell/wetwellsafety systems

Design & scale-up e.g. by U.S. NRC-standards

Alloy nanoparticles

ChemSheet PartialEQ

Surface Phase behaviour and melting

Design of functional nanomaterials

Surface properties of alloys

ChemSheet PartialEQ

Surface Surface tension and melting

Design of functional composite matrixes

Rotary kilns for cement, pigment, pig iron etc.

KilnSimu Non-EQ

Kinetic ChemEner profiles in kilns with variable feed

Scale-up and sustainable operation of rotary kilns

Flame structure in rotary kilns

Fluent-KilnSimu

Non-EQ

(kinetic) 2-way coupling of kiln bed and gas

Reduce kiln carbon footprint

ProMine to recover metal values from AMD

ChemSheet EQ - Interpretation of mine drainage titration

Recovery of value-added nanoparticles from mine waters

Paraequilibria ChemSheet P-EQ Paraeq. Phase formationin solidification

Improved steel / alloy property control

Magnetic eq. ChemSheet To be published

*CFE = ’Constrained Free Energy’ applies additional immaterial constraints in Gibbs energy minimisation

... there were a number of applications with ’CFE-use’

Background for constrained Gibbs energy models

multi-phase Gibbs free energy methods well established for mass-balance based equilibrium calculations (phase diagrams & multi-phase equilibria)

conventional mass-balance constrained techniques are not sufficient for

systems with immaterial entities and work effects

• surface energy problems

• material chemistry of fibre suspensions and membrane systems

• other force/field -related conditions

paraequilibrium systems constrained by transport phenomena

multi-phase problems which include extent of reaction or reaction rates

the constrained (Gibbs) Free Energy method (CFE) was developed to extend the availability of traditional multi-phase techniques to such ‘dynamic’ problems

The constrained Gibbs free energy method

With appropriate constraining the min(G) problemcan include:

reaction kinetics via extents of reaction [(r) with affinity (Ar)] surface energy [() with molar area (Ak)] Donnan (membrane) equilibria electrochemical & surface charge problems paraequilibrium systems magnetic field effects etc. J.W. Gibbs

1839 - 1903

kkkkkrrkk nAnFzAnpVTSG ddddddd ''

The novel constrained free energy method (CFE) brings the additional right hand terms into the Gibbs energy minimisation.

The general Gibbs energy equation is:

Examples of ‘constrained’ thermochemical systems

Process chemistry & fuel consumption in high-temperaturefurnaces (rotary kilns as example)

Composition of solidifying alloysand steel

Phase behaviour of (alloy) nano-particles

Rotary drums (as many other chemical reactors) are non-equilibrium systems.For one or several reactions there is:

Alloy or steel solidification is a diffusion-limited partial (or para) –equilibriumsystem:

Phase stability of (alloy) nanoparticles is affected by the surface energy ofthe material:

The common feature is to bring the respective constraints into min(G)

Conditions for the thermochemical modelling of constrained systems

Surface tension of Fe-O (1823-2023 K)

0.9

1.1

1.3

1.5

1.7

1.9

2.1

0.0001 0.001 0.01 0.1 1w% (O)

Surfa

ce te

nsio

n (N

/m)

1823 K1843 K1873 K1923 K1973 K2023 K1823K (Mukai 1992)1823K (Jimbo 1992)1823K (Kozakevitch 1961)1843K (Halden 1955)1853K (Ogino 1984)1873K (Mukai 1992)1873K (Kasama 1983)1873K (Gupt 1976)1923K (Mukai 1992)1923K (Keene 1982)1973K (Mukai 1992)2023K (Mukai 1992)

Surface Tension and Sorption over a Wide Temperature Range Surface tensions of various slags

300

350

400

450

500

550

600

650

700

300.0 350.0 400.0 450.0 500.0 550.0 600.0 650.0 700.0model prediction (mN/m)

expe

rimen

tal s

urfa

ce te

nsio

n (m

N/m

)

Comparison of predicted and measured aqueous surface adsorption coefficients

-8

-7.5

-7

-6.5

-6

-5.5

-5

-4.5

-4

-3.5

-3

-8 -7.5 -7 -6.5 -6 -5.5 -5 -4.5 -4 -3.5 -3

Log(Ki/a) predicted

Log(

Ki/a

) exp

erim

enta

l

Moltenslags> 1500 C

Salt melts200-1000 C

Aqueous-organicadsorption25 C

Surface tension and adsorption can becalculated for a variety of systems

The model requires an excess Gibbs energy(activity coefficient) model for both the bulk &surface layers

The GEmodel for the surface layer is typicallya modified version of the bulk model Oxygen

adsorptionon metals and alloys1100 C

surface tension (Ag-K)(Br-Cl) mixture at 1100 K

758595

105115125135145155165

0 0.5 1

Cl/(Cl+Br)

σ/(m

N/m

)

σ KCl-AgBr

σ AgCl-KBr

σ KCl-KBr

σ AgCl-AgBr

KCl-AgBr

AgCl-KBr

KCl-KBr

AgCl-AgBr

pure salts

R. Pajarre, P. Koukkari J.Colloid & Interface Sci. 2009

Applications for surface / interfacial energies

physical properties of alloys and mixtures

castability of (light metal) alloys

development of casting powders for steelmaking

models for slag foaming (when connected with viscosity data)

developing new (Pb-free) solder components

development of functional composite materials

microstructure modelling (dendrite formation insolidification)

…

Back diffusion and paraequilibria

In steel solidification and phase transformations in addition to thermodynamics also diffusion and convection control the phase formation.

To avoid computationally intensive diffusion calculations, the problems are often solved using approximate solutions based on some simplifying assumption such as:

– Full equilibrium (diffusion is assumed to be fast enough that equilibrium between solid and liquid is always maintained

– Scheil cooling, where once formed solid is assumed to be effectively inert. Liquid is assumed to completely mixed and local chemical equilibrium is assumed onthe advancing solidification front.

The ability in ChemSheet calculations to set constraints on arbitrary constituents or reactions or phase transformations allows also equally easy use of more sophisticated assumptions such as:

– Backdiffusion, where the small interstitial solutes in solid iron are assumed to beable to move between solid and liquid, while the larger metal atoms in solid areassumed immobile.

– Paraequilibrium, where new phases form with the same ratio of iron and alloyingelements than in the parent pahse.

Solidification with back diffusion calculation schema

Previously formed solid, partially “inert”

•no diffusion of at least some constituents to or

from melt

•Those constituents that can diffuse between solid and melt are assumed to

reach chemical equilibrium between the

phases

•Having all constituents inert in a solid phase

leads to Scheil-Gulliver behaviour, if none is inert, full equilibrium is reached

Remaining melt assumed to reach equilibrium (with the

effect of diffusive mass transfer from solid included) in the new

temperature. The corresponding amount of new

solid is formed

Calculation schema

Applying the outlined calculation procedure directly leads to system with one new solid layer (which has depending on the composition, temperature and thermochemistry one or more solid phases) for each cooling step.

This is initially cumbersome to implement, and becomes impossible after a while using ChemApp because of the number of components required.

Calculation schema

As a simplification, in the calculations it has been assumed that to calculate the chemical potential for the diffusive species in the solid, the layered structure can be replaced by one layer where the solid phases have a weighted average composition of those they have in the individual layers

It is still possible to keep track on the composition of the individual layers as they are formed

Back diffusion vs. Scheil cooling: result comparison

Temperature vs amount of solid formed

1200

1250

1300

1350

1400

1450

0 20 40 60 80 100

% solid

T/C

ParaequilibriumEquilibriumScheil

Composition: 10.84% Cr, 0.95% C. Cooling rate in experiment 0.167K/s

Cr fraction in the forming solid

0.07

0.08

0.09

0.1

0.11

0.12

0.13

0.14

0.15

0 20 40 60 80 100

% solid

Cr w

eigh

t fra

ctio

n in

the

mai

n so

lid (F

CC

) pha

se

paraequilibriumequilibriumScheil

Cr fraction in the forming solid

0.07

0.08

0.09

0.1

0.11

0.12

0.13

0.14

0.15

0 20 40 60 80 100

% solid

Cr w

eigh

t fra

ctio

n in

the

mai

n so

lid (F

CC

) pha

se

paraequilibriumequilibriumScheilExperimental

Back diffusionBack diffusion

ParaequilibriumIn paraequilibrium the metallic composition of the new growing phase is the same as in

the parent phase while carbon is able to equilibrate between the phasesFor a system with two metallic components, if initially all the metallic components are in

the parent phase, the constraint of equal relative amounts of components in the two phases is the same as requiring that the relative changes in the amounts of metallic components in one of the phases equals their relative amounts in the system.

For example:

The latter form is mathematically identical to a mass balance relation indicating that the required constraint can be included in the stoichiometric matrix with coefficients that match the (relative) molar feed amounts

0

0

FCCFeNi

FCCNiFe

FCCFeNiFeNi

FCCNiNiFeFeNiFe

FCCNi

FCCFe

dnudnu

dnnnndnnnnnndndn

Fe Ni C *Ni-FCCFe:C 1 1 -uNi

Fe 1 -uNi

Ni:C 1 1 uFe

Ni 1 uFe

Fe:C 1 1Fe 1Ni:C 1 1Ni 1

FCC

BCC

(stoichiometric descriptionfor a paraequilibriumbetween FCC and BCCphases)

Paraequilibrium

From the stoichiometric matrix it follows that in the calculated paraequilibrium state– (Carbon is in equilibrium between the phases)

–

(Weighted average of chemical potentials of iron and alloying metals are the same,cf M. Hillert: Thermodynamics and phase transformations, EDP Sciences, 2006

Chpt. 14.5 )

When the number of phases N or the number of metallic components M is larger than two the number of required constraint components is given by (N-1)*(M-1)

BCCC

FCCC

Fe Ni C *Ni-FCCFe:C 1 1 -uNi

Fe 1 -uNi

Ni:C 1 1 uFe

Ni 1 uFe

Fe:C 1 1Fe 1Ni:C 1 1Ni 1

FCC

BCC

BCCNiNi

BCCFeFe

FCCNiNi

FCCFeFe uuuu

Multiphase models for rotary kilns

BAT for clinkering, calcination, chemical recovery etc. rotating steel drums with little measured data available

Counter-current rotary drums:

combined chemistry & energy simulation needed !

Rotary kiln model (KilnSimu)

Gas in

Bed out

CelliCell1 CellN

Bed in eq.

Rotary Kiln

dust+volatiles

Gas out

Bed in

Mass transfer of gas speciesbetween bed and gas.Convection and diffusionbetween gas and bed.

Mass transfer of condensedspecies between bed and gas.Dusting of solid particles andformation of liquid phases.

convection+diffusion

dust+volatiles

convection+diffusion

dust+volatiles

convection+diffusion

•Axial plug flow model is used for both the bed and the gas in each cell.•The variables in each cell are the temperature of bed, gas, inner and outer wall andthe mass flows of species in bed and gas.•The bed and gas output from each cell is in thermodynamic equilibrium but theymay contain non-reactive parts that are calculated with kinetics.

condensed condensed condensed

Bed in eq. Bed in eq.

Gas in eq. Gas in eq. Gas in eq.

Cement kiln – a multiphase system with several kinetic restrictions

System Fe-Ca-K-Cl-S-Si-Al-Mg-Na-O-N-C-H18 2 32 8 43

Fe Ca K Cl S Si Al Mg Na O N C H R_C R_CaCO3 R_CaO R_Ca3SiO5 R_SiO2

The thermodynamic system contains elements Fe-Ca-K-Cl-S-Si-Al-Mg-Na-O-N-C-H consisting of gas, slag and pure condensed phases with altogether 78 species

Kinetic restrictions are combined e.g. for 5 species including carbon (used as fuel), CaCO3, CaO, SiO2, Ca2SiO2 and Ca3SiO5 phases.

C + O2 → CO2

CaCO3 → CaO + CO2

2CaO + SiO2 → Ca2SiO4

CaO + Ca2SiO4 →Ca3SiO5

SiO2 + CaxSiyOz → CaxSiy+1Oz+2

Component listing from the

cement kilnFactSage file

Components in red are virtualand control 5

kinetically slowreactions

Kineticallyconstrained

reactions

Kinetics with the virtual components

C Ca O R_CaCO3

CO2 1 0 2 0

CaO 0 1 1 0

CaCO3 1 1 3 1

+ 0 0 0 1

- 0 0 0 -1

Kineticallyconstraineddecomposition of calcite:

CaCO3 → CaO + CO2

Simulated temperature and phase profiles

the kiln is divided into 100-150 cells in axial direction

the cell volume elements of the material bed and gas described as open systems

simulation by min(G) combinedwith the mass and heat transfer between the volume elements and their surroundings.

1-Dimensional profiles receivedfor:

temperatures bed & gas composition radial heat fluxes kiln holdup etc.

Courtesy of Ube Industries Dr. Morihisa Yokota

Illustration of the coupling: KilnSimu Fluent

Data calculated by KilnSimu are transferred to Fluent by FKS

A. Bed feed surface: Temperature boundary condition calculated by KilnSimu

B. Gas volume: Mass flow (e.g. CO2) sources on bed feed surface calculated by KilnSimu

C. Drum wall: Temperature boundary condition calculated by KilnSimu

Drum shellDrum liningGas volumeFeed volume

A. C.B.

Accurate 3 D description of the entire kiln• Flame position and gas phase phenomena: Fluent• Bed multiphase phenomena: KilnSimu• Coupled solution for global convergence: FKS

Improved resource efficiency for BAT rotary kilns• Control of emissions • Lower carbon impact (by use of

biofuel etc.)• Reduced energy consumption

Benefits of the coupled simulation

www.kilnsimu-fks.com

Uses of rotary kiln simulations

•Demand of combustion air•Study of various fuels•Optimization of fuel consumption •Optimization of gas circulation•Optimization of energy efficiency•Phase formation studies (material bed and gas composition)•Exit bed composition•Exit gas composition•Kiln scale-up

Summary

ChemApp has been used for various intricate problems leading tomaterial and energy saving solutions

ChemSheet and Kilnsimu used as major platforms

Methods are yet generic & applicable in any other min(G) software

Process simulation based on both global equilibrium and constrainedtechniques

CFE can be applied in any ChemApp procedure by using the program CSFAP for the necessary datafile manipulation

Acknowledgements

Technology and Innovation Agency of Finland - TEKES

Academy of Finland

Colleagues at GTT & elsewhere

Particular thanks to Gunnar Eriksson for his contributions and patience

while developing new CFE applications for ChemApp!